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Postdoctoral Fellow (Incoming) Assistant Professor Email: penny.ling.pan [@] gmail [DOT] com |
Prospective Students: I am actively looking for self-motivated students (including undergraduate/graduate students and research assistants) who are interested in the areas of artificial intelligence, machine learning, deep reinforcement learning, generative flow networks, and multi-agent systems. I have several PhD/MPhil/RA openings starting in Spring/Fall 2024 at HKUST. Please drop me an email with your CV if you are interested.
My research interests mainly include theoretical understanding, algorithmic improvements and practical application of generative flow networks (GFlowNets), reinforcement learning and multi-agent systems. I focus on developing robust, efficient, and practical deep reinforcement learning algorithms. I am also interested in the application of reinforcement learning in practical problems like computational sustainability and drug discovery.
Pre-Training and Fine-Tuning Generative Flow Networks
Ling Pan, Moksh Jain, Kanika Madan, Yoshua Bengio
Preprint
[PDF]
Distributional GFlowNets with Quantile Flows
Dinghuai Zhang*, Ling Pan*, Ricky T.Q. Chen, Aaron Courville, Yoshua Bengio
Preprint
[PDF] [Code]
One is More: Diverse Perspectives within a Single Network for Efficient Deep Reinforcement Learning
Yiqin Tan, Ling Pan, Longbo Huang
Preprint
[PDF]
Beyond Conservatism: Diffusion Policies in Offline Multi-agent Reinforcement Learning
Zhuoran Li, Ling Pan, Longbo Huang
Preprint
[PDF]
Let the Flows Tell: Solving Graph Combinatorial Problems with GFlowNets
Dinghuai Zhang, Hanjun Dai, Nikolay Malkin, Aaron Courville, Yoshua Bengio, Ling Pan
In Thirty-Seventh Conference on Neural Information Processing Systems (NeurIPS), New Orleans, USA, 2023
Spotlight (Top 5%)
[PDF] [Code]
Better Training of GFlowNets with Local Credit and Incomplete Trajectories
Ling Pan, Nikolay Malkin, Dinghuai Zhang, Yoshua Bengio
In Fortieth International Conference on Machine Learning (ICML), Hawaii, USA, 2023
[PDF] [Code]
Stochastic Generative Flow Networks
Ling Pan*, Dinghuai Zhang*, Moksh Jain, Longbo Huang, Yoshua Bengio
In Thirty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI), Pittsburgh, USA, 2023
Spotlight (Top 7%)
[PDF] [Code]
Generative Augmented Flow Networks
Ling Pan, Dinghuai Zhang, Aaron Courville, Longbo Huang, Yoshua Bengio
In Eleventh International Conference on Learning Representations (ICLR), Kigali, Rwanda, 2023
Spotlight (Top 5%)
[PDF] [Code]
RLx2: Training a Sparse Deep Reinforcement Learning Model from Scratch
Yiqin Tan*, Pihe Hu*, Ling Pan, Jiatai Huang, Longbo Huang
In Eleventh International Conference on Learning Representations (ICLR), Kigali, Rwanda, 2023
Spotlight (Top 5%)
[PDF] [Code]
E-MAPP: Efficient Multi-Agent Reinforcement Learning with Parallel Program Guidance
Can Chang, Ni Mu, Jiajun Wu, Ling Pan, Huazhe Xu
In Thirty-Sixth Conference on Neural Information Processing Systems (NeurIPS), New Orleans, USA, 2022
Spotlight (Top 5%)
[PDF] [Website]
Plan Better Amid Conservatism: Offline Multi-Agent Reinforcement Learning with Actor Rectification
Ling Pan, Longbo Huang, Tengyu Ma, Huazhe Xu
In Thirty-Ninth International Conference on Machine Learning (ICML), Baltimore, USA, 2022
[PDF] [Code] [Website]
Recurrent Softmax Policy Gradient for Delay-Constrained Scheduling
Pihe Hu, Ling Pan, Yu Chen, Zhixuan Fang, Longbo Huang
In Twenty-Third International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (MobiHoc), Seoul, South Korea, 2022
[PDF]
Network Topology Optimization via Deep Reinforcement Learning
Zhuoran Li, Xing Wang, Ling Pan, Lin Zhu, Zhendong Wang, Junlan Feng, Chao Deng, Longbo Huang
IEEE Transactions on Communications (TCOM), 2022
[PDF]
Regularized Softmax Deep Multi-Agent Q-Learning
Ling Pan, Tabish Rashid, Bei Peng, Longbo Huang, Shimon Whiteson
In Thirty-Fifth Conference on Neural Information Processing Systems (NeurIPS), 2021
[PDF][Code]
Exploration in Policy Optimization through Multiple Paths
Ling Pan, Qingpeng Cai, Longbo Huang
Journal of Autonomous Agents and Multi-agent Systems (JAAMAS), 2021
[PDF]
Softmax Deep Double Deterministic Policy Gradients
Ling Pan, Qingpeng Cai, Longbo Huang
In Thirty-Fourth Conference on Neural Information Processing Systems (NeurIPS), 2020
[PDF][Code]
Reinforcement Learning with Dynamic Boltzmann Softmax Updates
Ling Pan, Qingpeng Cai, Qi Meng, Wei Chen, Longbo Huang
In Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI), 2020, Yokohama, Japan
(Acceptance rate: 12.6%)
[PDF]
Multi-Path Policy Optimization
Ling Pan, Qingpeng Cai, Longbo Huang
In Nineteenth International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), 2020, Auckland, New Zealand
Invited for fast-track publication in JAAMAS (Top 5%)
[PDF]
Deterministic Value-Policy Gradients
Qingpeng Cai*, Ling Pan*, Pingzhong Tang
In Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), 2020, New York, USA
[PDF]
A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems
Ling Pan, Qingpeng Cai, Zhixuan Fang, Pingzhong Tang, Longbo Huang
In Thirty-Third AAAI Conference on Artificial Intelligence (AAAI), 2019, Hawaii, USA
(Acceptance rate: 16.2%)
[PDF] [Slides] [Poster]
Probabilistic Generative Modeling for Procedural Roundabout Generation for Developing Countries
Zarif Ikram, Ling Pan, Dianbo Liu
Preliminary version in NeurIPS 2023 RealML Workshop
[PDF]
Learning to Scale Logits for Temperature-Conditional GFlowNets
Minsu Kim, Joohwan Ko, Dinghuai Zhang, Ling Pan, Taeyoung Yun, Woochang Kim, Jinkyoo Park, Yoshua Bengio
NeurIPS 2023 AI4Science Workshop
[PDF]
Outstanding Doctoral Thesis, by Tsinghua University, 2022
Thesis: Towards Robust, Efficient, and Practical Deep Reinforcement Learning Algorithms
Outstanding Graduate (top 3%), by Tsinghua University, 2022
Also Beijing outstanding graduate and IIIS, Tsinghua University outstanding graduate, 2022
China National Scholarship (top 2%), by Ministry of Education of China, 2021
Microsoft Research Ph.D. Fellowship (Asia), 2020
12 outstanding Ph.D. students in computer science in the Asia-Pacific region
China National Scholarship (top 2%), by Ministry of Education of China, 2016
China National Scholarship (top 2%), by Ministry of Education of China, 2015
China National Scholarship (top 2%), by Ministry of Education of China, 2014
Organizer:
SPC member:
PC member/Reviewer:
Towards Robust, Efficient, and Practical Reinforcement Learning Computer Science and Artificial Intelligence Lab (CSAIL), MIT, December, 2021 Berkeley Artificial Intelligence Research (BAIR), UC Berkeley, November, 2021
Regularized Softmax Deep Multi-Agent Q-Learning Reinforcement Learning China Community (RLChina), May, 2022 AI Time NeurIPS Session (by Tsinghua University), February, 2022 Third International Conference on Distributed Artificial Intelligence, January, 2022
Softmax Deep Double Deterministic Policy Gradients IJCAI-Shanghai Artificial Intelligence Industry Association (SAIA) Young Elite Symposium, July, 2021 Second International Conference on Distributed Artificial Intelligence, October, 2020
A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems First International Conference on Distributed Artificial Intelligence, October, 2019 Nanjing University, May, 2019